Multi-Class Freeway Congestion and Emission Based on Robust Dynamic Multi-Objective Optimization

In order to solve the problem of traffic congestion and emission optimization of urban multi-class expressways, a robust dynamic nondominated sorting multi-objective genetic algorithm DFCM-RDNSGA-III based on density fuzzy c-means clustering method is proposed in this paper. Considering the three performance indicators of travel time, ramp queue and traffic emissions, the ramp metering and variable speed limit control schemes of an expressway are optimized to improve the main road and ramp traffic congestion, therefore achieving energy conservation and emission reduction. In the VISSIM simulation environment, a multi-on-ramp and multi-off-ramp road network is built to verify the performance of the algorithm. The results show that, compared with the existing algorithm NSGA-III, the DFCM-RDNSGA-III algorithm proposed in this paper can provide better ramp metering and variable speed limit control schemes in the process of road network peak formation and dissipation. In addition, the traffic congestion of expressways can be improved and energy conservation as well as emission reduction can also be realized.

Coordinated Perimeter Flow and Variable Speed Limit Control for Mixed Freeway and Urban Networks

Recent studies have leveraged the existence of network macroscopic fundamental diagrams (MFD) to develop regional control strategies for urban traffic networks. Existing MFD-based control strategies focus on vehicle movement within and across regions of an urban network and do not consider how freeway traffic can be controlled to improve overall traffic operations in mixed freeway and urban networks. The purpose of this study is to develop a coordinated traffic management scheme that simultaneously implements perimeter flow control on an urban network and variable speed limits (VSL) on a freeway to reduce total travel time in such a mixed network. By slowing down vehicles traveling along the freeway, VSL can effectively meter traffic exiting the freeway into the urban network. This can be particularly useful since freeways often have large storage capacities and vehicles accumulating on freeways might be less disruptive to overall system operations than on urban streets. VSL can also be used to change where freeway vehicles enter the urban network to benefit the entire system. The combined control strategy is implemented in a model predictive control framework with several realistic constraints, such as gradual reductions in freeway speed limit. Numerical tests suggest that the combined implementation of VSL and perimeter metering control can improve traffic operations compared with perimeter metering alone.

Traffic Flow Modeling of Freeway Variable Speed Limit Control Based on the Big Data of Driving Behavior

Variable speed limit (VSL) control is a flexible restriction on the rate at which motorists can drive on a given stretch of road. Effective VSL control can increase safety and provide clear guidance for motorists. Previous traffic flow models of VSL control were mostly based on the influence of VSL on average speed (macro) or driver’s expected speed (micro). Few models considered the influence of VSL on driver’s actual driving behavior. In this paper, we first briefly introduce the big traffic data involved in this study and explain the mapping relationship between the data and driving behavior. Then, we analyze the driver’s actual driving behavior under the VSL control. Then, an improved single-lane cellular automaton model is established based on the driving behavior characteristics under VSL control. After that, we calibrate the parameters of the single-lane cellular automaton model with the left lane as the calibration object. Finally, this paper uses the proposed single-lane cellular automaton model to simulate the traffic flow characteristics under VSL control. The numerical simulation results show that the simulation of the variable speed limit in different density intervals presents different results, but these results are consistent with the actual situation of variable speed limit control, which verifies the validity of the proposed model.

An Overview of Reinforcement Learning Methods for Variable Speed Limit Control

Variable Speed Limit (VSL) control systems are widely studied as solutions for improving safety and throughput on urban motorways. Machine learning techniques, specifically Reinforcement Learning (RL) methods, are a promising alternative for setting up VSL since they can learn and react to different traffic situations without knowing the explicit model of the motorway dynamics. However, the efficiency of combined RL-VSL is highly related to the class of the used RL algorithm, and description of the managed motorway section in which the RL-VSL agent sets the appropriate speed limits. Currently, there is no existing overview of RL algorithm applications in the domain of VSL. Therefore, a comprehensive survey on the state of the art of RL-VSL is presented. Best practices are summarized, and new viewpoints and future research directions, including an overview of current open research questions are presented.